BMF CP112: Household Structure and Solar Energy Adoption Willingness across Urban and Rural United States
Puffleg Hoary
July 24, 2025
“[…] the age of technology has arrived, and Kingfisher has decided it’s time for something new: Technological Innovation. Innovation can help Kingfisher conserve energy while maintaining a sense of tranquility, which is suitable for an increasingly advanced age with diminishing physical strength.”
—In “Innovation,” Wild Wise Weird (2024)
1. Project description
1.1. Main objectives
The current study is conducted to examine the following research questions:
- How is the household’s member number associated with its willingness to adopt solar energy?
- Is the relationship between the household’s member number and its willingness to adopt solar energy conditional on the proportion of children within the household?
- Is the relationship between the household’s member number and its willingness to adopt solar energy conditional on the proportion of older adults within the household?
- How is the household’s income associated with its willingness to adopt solar energy?
- How is the household’s house size associated with its willingness to adopt solar energy?
- Is the relationship between the household’s house size and its willingness to adopt solar energy conditional on the age of the house?
- What are the differences in these relationships between families residing in urban and rural areas?
Findings from this study are expected to contribute to promoting the eco-surplus culture and sustainable development [1].
1.2. Materials
The Granular Interaction Thinking Theory (GITT) will be employed for the conceptual development of this study, while the Bayesian Mindsponge Framework (BMF) analytics will be utilized for statistical analysis [2,3]. The dataset comprises responses from 9919 U.S. residents of single-family and small multifamily homes [4]. Statistical analyses will be conducted using the bayesvl R package, which utilizes the Markov chain Monte Carlo (MCMC) algorithm for estimation [5].
For the sake of research transparency and reducing research and reproducibility costs, we have stored all data and computer code on Zenodo: https://zenodo.org/records/16406652.
1.3. Main findings
The preliminary analysis indicates that households with larger houses are positively associated with the willingness to adopt solar energy, but the effect declines among houses with higher age (see Figure 1).
Figure 1: The estimated posterior distributions.
2. Collaboration procedure
Portal users should follow these steps to register to participate in this research project:
- Create an account on the website (preferably using an institutional email).
- Comment your name, affiliation, and your desired role in the project below this post.
- Patiently wait for the formal agreement on the project from the AISDL mentor.
If you have been invited to join the project by an AISDL member, you are still encouraged to follow the above formal steps.
All the resources for conducting and writing the research manuscript will be distributed upon project participation.
AISDL mentor for this project: Minh-Hoang Nguyen.
AISDL members who have joined this project: Quan-Hoang Vuong, Viet-Phuong La.
The research project strictly adheres to scientific integrity standards, including authorship rights and obligations, without incurring an economic burden at participants’ expenses.
References
[1] Vuong QH. (2021). The semiconducting principle of monetary and environmental values exchange. Economics and Business Letters, 10(3), 284-290. https://reunido.uniovi.es/index.php/EBL/article/view/15872
[2] Vuong QH, Nguyen MH. (2024). Better economics for the Earth: A lesson from quantum and information theories. https://books.google.com/books?id=I50TEQAAQBAJ
[3] Vuong QH, Nguyen MH, La VP. (2022). The mindsponge and BMF analytics for innovative thinking in social sciences and humanities. Walter de Gruyter GmbH. https://www.amazon.com/dp/8367405102/
[4] Fuentes TL, et al. (2025). A dataset for understanding self-reported patterns influencing residential energy decisions. Scientific Data, 12, 1273. https://www.nature.com/articles/s41597-025-05335-8
[5] Vuong QH, La VP. (2025). Package ‘bayesvl’ version 1.0.0. https://books.google.com/books/about?id=znleEQAAQBAJ
[6] Vuong QH. (2024). Wild Wise Weird. https://www.amazon.com/dp/B0BG2NNHY6
tags:
solar energy